1. Field of the Invention
The present invention relates to image analysis with the conceptual interpretation of features identified in complex scenes. More specifically, the field of the invention is bone imaging.
2. Prior Art
Image segmentation is used to identify relevant features in an image of body tissue such as an x-ray, MRI, or ultrasonic image. The data are gray levels, or a finite set of colors, on a rectangular lattice of picture elements (pixels), and correspond to digitized values of radiated intensity from some photochemical or photoelectric sensor. Image segmentation algorithms are used in the context of classification where the entities to be classified are pixels or groups of pixels. These algorithms assign to each pixel a unique number (label) representing membership to a set of pixels that define an object or region in the image. Image segmentation has applications in photon emission tomography, magnetic resonance imaging, remote sensing by satellites, and object/background discrimination.
There are three requirements of a good image segmentation technique:
1) Each resulting segmented region or pixel group should be as homogeneous as possible-typically in terms of pixel intensities;
2) Pixels in different regions should be non-homogeneous; and
3) The resulting groups should have some scene-specific meaning, such as objects, background, etc.
A text by Schalkoff (Schalkoff, R. J., Digital Image Processing and Computer Vision. Wiley, N.Y., 1989), which is incorporated by reference herein, separates segmentation algorithms into two classes. In noncontextual segmentation, spatial relations among pixels or regions are ignored; though often computationally efficient, the performance of these techniques is adversely affected by noise in the image recording process and multiple features of similar intensity included in a scene. In contextual segmentation, the segmentation process employs neighboring relations among pixels and regions. Contextual classification is often more successful in fulfilling the three goals above because the local image information typically reinforces a classification decision.